2019/02/07 by Nir Diamant, Diamant, Nir, Dean Zadok +8 · 1 voice · 1 citation
Computer Science · Psychology · #Computer Vision and Pattern Recognition (cs.CV) #Evolutionary Psychology and Human Behavior #FOS: Computer and information sciences #Face recognition and analysis #Generative Adversarial Networks and Image Synthesis #cs.CV
paper · pdf · doi:10.48550/arxiv.1902.02593
openalex publication_date 2019/02/07 · arxiv published 2019/02/07 · arxiv updated 2019/02/25 · openalex created_date 2022/07/29 · openalex updated_date 2026/07/28
Beauty is in the eye of the beholder. This maxim, emphasizing the\nsubjectivity of the perception of beauty, has enjoyed a wide consensus since\nancient times. In the digitalera, data-driven methods have been shown to be\nable to predict human-assigned beauty scores for facial images. In this work,\nwe augment this ability and train a generative model that generates faces\nconditioned on a requested beauty score. In addition, we show how this trained\ngenerator can be used to beautify an input face image. By doing so, we achieve\nan unsupervised beautification model, in the sense that it relies on no ground\ntruth target images.\n